Factors Associated With Permanency for Children in Out-Of-Home Placement: A Survival Analysis

Haksoon Ahn1, Kimberly Williams1, Jinyung Kim1

  • 1School of Social Work, University of Maryland, Baltimore, MD, USA.

Child Maltreatment
|November 29, 2023
PubMed

Insights

Understanding factors influencing child welfare permanency is crucial. This study identified race, age, placement stability, and family engagement as key determinants affecting reunification, guardianship, and adoption timelines.

Area of Science:

  • Child Welfare Research
  • Social Work
  • Public Health

Background:

  • Achieving permanency for children in foster care is a primary objective of child welfare systems.
  • Timely permanency is essential for child well-being and successful outcomes.
  • Identifying factors influencing permanency is critical for improving child welfare services.

Purpose of the Study:

  • To examine the duration children spend in foster care.
  • To identify key determinants associated with achieving permanency outcomes.
  • To analyze factors influencing reunification, guardianship, and adoption.

Main Methods:

  • Utilized administrative data from a single state over a six-year period.
  • Included a final sample of 1,874 children.
  • Employed multivariate survival analyses, specifically Cox proportional hazards regression models.

Main Results:

  • Median time to permanency varied: 188 days for reunification, 505 days for guardianship, and 932 days for adoption.
  • Significant factors included race/ethnicity, age at removal, number of placement changes, number of siblings, family team decision meetings (FTDM), and placement type.
  • These determinants significantly influenced the achievement of permanency.

Conclusions:

  • Findings highlight the need to address racial disproportionality in child welfare.
  • Emphasizes the importance of family engagement strategies, such as FTDM.
  • Suggests policy and practice implications for kinship care and placement stability to expedite permanency.

Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
134
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
247
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
87
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
197
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
79
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
448